{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'D:\\\\Mine\\\\Py\\\\DC_PV_Power_Predict_2018\\\\DC_Data\\\\'\n",
    "\n",
    "data_train1 = pd.read_csv( data_dir + 'train_1.csv' )\n",
    "data_train2 = pd.read_csv( data_dir + 'train_2.csv' )\n",
    "data_train3 = pd.read_csv( data_dir + 'train_3.csv' )\n",
    "data_train4 = pd.read_csv( data_dir + 'train_4.csv' )\n",
    "data_train = pd.concat( [data_train1, data_train2, data_train3, data_train4] ) \n",
    "\n",
    "data_test1 = pd.read_csv( data_dir + 'test_1.csv' )\n",
    "data_test2 = pd.read_csv( data_dir + 'test_2.csv' )\n",
    "data_test3 = pd.read_csv( data_dir + 'test_3.csv' )\n",
    "data_test4 = pd.read_csv( data_dir + 'test_4.csv' )\n",
    "data_test = pd.concat( [data_test1, data_test2, data_test3, data_test4] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    时间  辐照度        风速   风向        温度        压强        湿度  \\\n",
      "0  2016-04-01 00:00:00 -1.0 -0.707547  251 -0.090909 -0.030303 -0.157895   \n",
      "1  2016-04-01 00:15:00 -1.0 -0.707547  251 -0.090909 -0.030303 -0.157895   \n",
      "2  2016-04-01 00:30:00 -1.0 -0.716981  250 -0.107071 -0.030303 -0.136842   \n",
      "3  2016-04-01 00:45:00 -1.0 -0.726415  248 -0.123232  0.030303 -0.094737   \n",
      "4  2016-04-01 01:00:00 -1.0 -0.735849  244 -0.135354  0.030303 -0.073684   \n",
      "\n",
      "   实发辐照度      实际功率  \n",
      "0    0.0 -0.019333  \n",
      "1    0.0 -0.019333  \n",
      "2    0.0 -0.021000  \n",
      "3    0.0 -0.022000  \n",
      "4    0.0 -0.022000  \n"
     ]
    }
   ],
   "source": [
    "print( data_train.head() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id                       时间  辐照度        风速   风向        温度        压强  \\\n",
      "0   1  2018-04-30 23:59:59.985 -1.0 -0.801887    8 -0.046465  0.272727   \n",
      "1   2  2018-05-01 00:14:59.985 -1.0 -0.783019    2 -0.058586  0.272727   \n",
      "2   3  2018-05-01 00:29:59.985 -1.0 -0.783019  358 -0.066667  0.272727   \n",
      "3   4  2018-05-01 00:44:59.985 -1.0 -0.773585  351 -0.078788  0.272727   \n",
      "4   5  2018-05-01 00:59:59.985 -1.0 -0.754717  344 -0.090909  0.272727   \n",
      "\n",
      "         湿度  \n",
      "0  0.221053  \n",
      "1  0.221053  \n",
      "2  0.221053  \n",
      "3  0.242105  \n",
      "4  0.242105  \n"
     ]
    }
   ],
   "source": [
    "print( data_test.head() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x149a0b61048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qg4f7l8U+HUwIFwJi799uB6LQU46bzBOnjID+3oLlyDdzBXfc08GEcGFEr/YkiohCTzFu+8+4SYHce3SiwY9urhlQ/SjI3E/BDVkipdER5qSiqCEKPcW4Ldxyk2PfrFtGPDGCGyrMlkZHFJvihUnkg6JC66jSs7wUbqmC08axVYrZ7JYBqj8iUhwkuMUqldXcSiJqTfHCRiz0hGNnwbRaLWc+thX1bpn+3gL2bbwBBclWEBxYt7IbxwfXYFoRTTeMDpVRYnQCTZulLgo94dhZMK22B7CbHmRXmh3F1g1CdMgQ0LeoC4DauGDAscd/Gt0votATjp1bxW3/GVXQyU4x79t4g+00IyE5ZMk5tK1lCTsGelytzqb5QpsHVewFqCrs371/vqFpl5moNvMLClHoCcdNyuG+jTfgzcE1lkq4GZcN6e9TIfnEycGYQuWkqMsVnlkV2ilgA6MIzWx0WB53mjF39izbz0/TilAUesIJwq1idtlY3ZoM+9a7IydOu/psIfqUyhXcv+sQFn8kp7SkDYxV4bbbl6PTwyAJw+hQ/QycLZVtYzNpWhGKQk84rbT1tWtXa9ycquwWK6toeLSInq0/kWlDCaPCjH1vnMbV3RfbWsoX5zSsGnwR9w6NoWP2LOwY6EE+Z63YrbY7rTajNpMgDBy7LRLRHAD/BOAiVNMcn2LmzUR0GYAfAugC8AsAf8LM5+yO5bXbomBNkF3ijGMXJ0sNU9TNdHZo6Jg9S6nwjdfNo+7Mo+mE5JElwhvf/AIeHD6MnfvHa64dLUMAoWY4SU7L4rZrChj6+Vs1g6G1DGHbHY3DxN10X41TB0UvuO226EahE4C5zPw7ItIA/DOArwG4D8DTzPxDIvoOgEPM/Hd2xxKF3jpBthR2apdroGUJsJnO7vS6kFx2DPR4GuJtDHh2q4STqrCd8E2h1x20A1WF/ucAXgDwcWY+T0TXAdjCzKvt3t+MQl+88QVP+xtkCLju8i4cf7eEk5MlzMoA5Wn79+RzGrbcshQA8MAzh/HeudqLsrNDw+abl85cQA8OH25wH1w0K4MPzlc/KKdlQACm9A+eOzuLaWaU9OedHRrWfHI+9h6daLB0CcAcLYNSeXqmuKKQz2Fy6lyDXABAujldP/g536GBudqm1nwc40YYHi1atsdtFs3FeRaSSZYIH794jqfCMQLw5uCa4IRKCL4qdCLKAjgI4PcBfBvANgD7mfn39dcXAvhHZv4Du+N4VejNKvNWyFB1UKzqrGhZwrbbl8d+8rxquSsIVti53wzWrexucLU44ffQiqTiVqG7Cooyc4WZewBcCuBaAP/OajeFIPcQ0QgRjUxMTLj5uFCZtlHmwIX0qycOxHvyfKlcweP7x0WZC44U8jlsH+ixzUxZdUUXHu5fZlvoU0/aApbtwFOWCzNPAngJwEoAeSIyesFcCuCk4j2PMnMfM/fNmzevFVkjw8nJkkzXEVLD1LnzGDlxGh2z1a2ffv7mGSx96P94GmJSKlewfmgslSX6QeGo0IloHhHl9cc5AH8E4FUAewHcru92N4AfBSVk1FiQz7mqjhOEJHBmqozH94/b+sbL02wZ23FDcbKEDU8dEqXuA24s9PkA9hLRKwBeBvBTZn4ewDcA3EdErwP4CIDvBSdm+9Czq5RoWcKG1Utw14qFbZMpqmgZkn7mgi+UKzxT7i80j2P7XGZ+BUCvxfZfoepPD4zjg2sim+ViZLrYBUZbzXIBrP35pAdujayVTj2T5WypjAU2mTBesWpdakb874KfyMzZ1ol8P/Tjg2swPFrEhqcO1RQlqPArJ9vN+x/uX4a+RV2B5oVbfe9ZRNh2Z2Phhfl9XnKBrchpWSkCEoSYEYvS/217jrlS5kD7u6u5nfpjhdPorP7eAuZaBKLK09xwfPOxtu05htuuKbTk5y+VK5AwgdBOVG0ABPfEQqF77ZbWzu5qXqb+mHE7Oks1XNl8fKtj7T5YxF0rFlZLrptEEnmEdjJZKkvGS4vEQqF77ZbWzu5qzU79cWvZuzm+6lh7j07gQ3Mi71UTEk5Oy2LVFV3K180mRxqHUvhJLBS62x7KQPuLFZrt8ObWsndzfLtjTUqgSQgRo7vn8Xetr9EsUUPgP21DKfwkFgrdqoeyod47OzTkc5rn1rB+ytZMe1q3lr2b49sdK029oIVokSWaGZqiMjpUWVRpGkrhJ7FZj6umzkeBZmTbsHqJZXaMlWXvdHynY907NOapv4Yg+IGhrO3cJ6rUWL8NkbR0aYyFhZ5EWhk84eVYdkMoBCFIskQYHi3iPoVBoWUId61YGPhQCrcJCEnAU/vcVpF+6O7x06JYNfiip5amgmBmVoZwvokisnUru/H8oVPK1sydHRpGH7rR9lr34z5QXf9x6vToa7dFob34bVFIRzuhFbwq8ywR1q3sxsP9y2z77DsF7P26D5pNLY4jsfGhpwm7lEYv1ol5nJwgBMmsDOFvLMbG2bEgn2uoajaUNuDffbAgn7O8B5KYMCAKPYL4YVF4aZcgCM1ScHCDdHZoyh4txug5ldL2ch/YuWa8JCDEHXG5RJBmi5XMbH3uiChzIVA6O7SZtEQVm29eallDsm5lt206o6GYrajf7uSa8TMBIeqIhR5BrCwKQvVCXTX4YoNFVD8X1M4qEgS/cFO0ZlynKuvZzh3i1rJ245qJctqzn4hCjyDmm6A4WaqZ52j2MRpDnjc8eaimla0oc6EduF0x2ilTO6Xt9GNgkKagpxOi0JugHUUKxk1glXJltj627TkmfcmFtkPwJ3vKSWm7sazTFPR0QnzoHml3kYKT9ZFGK0QIH4a7mQFu6O8tYMPqJViQz+HkZAnb9hzzdD81208piYiF7hG/Uqnc4mR9qF4XhCApWAQmraxsN6tZu9RFN/eUW9dMGhCF7hG/UgqNiy9fNz6u/kJ0CgxtWL2kwYcuCEFjtn5VCnnkxGnsPlh0VNR+GElpCXo6IS4Xj7SaUljvsjkzVcZkqax03zilXPX3FjBwrQysFtoHwZ1Cfnz/uOX2+3cdqpnSpTKGjKyuJPZcCQqx0D3SapGC1cVvxsoyqbc+jHFzhoX/u/fPN/FNBKE51q7srnnuNY5jdFc0DJiLc5qyRYBX90vaEQvdI60WKbi5+O32MSpAzRa+uFuEdrL36ESN1ZzvaH4WqDG7tj6oWb+PDLxwh1joTdCKv85NENPOfSMVoELYmK1mAC2vECenytg+0GPbd0iyudwhFnqbsUqxMqNlSOm+GR4tStGQ0BacBj4aVrMfdRAZItw7NAYAyOesrf005pQ3g1joAWPueGhMZ+ns0PB+uWLZ9P9Dc2ZZWv9GMFUQgiSnZfHNW5cBuJAGqFLXflnNZp+6liVoGar5kUhrTnkziEIPkPp0LuPCtbOyVf0xnIKpgtAMGQI+PEezTJs1/lcNiDCsZq91EEYrC6vxc+VK1eDpmD2rJqfckCPteeZOiMslQJpRwqqlpfgQk4eTW6MdTDPwwflpbB/oUXZOtKvEVL3WaRMoZVSTCaYV09LOTJUblHlaRsi1iij0APGqhM0dFc0X6/BoERSFu1/wjZyWwaev6ApbDADOWSROM2utXtt881LbWJFde1wANYp763NHlIVHQi3icgkQr2X59R0VR06ctp3JKMSX98vT+MX42bDFmKHe+LAq2VfN37TL+rp/16EGtwqgbo9bT6lcUb4uq9ZGxEIPEKeMFjtK5Qp27h8XZZ5QGIhUTMRsLfvVgK6/t4BH7lyudNeYrftWZRaqiEIPkPoLNuvRbyLZ5kK7uP6qeTOPVaX864fGPJfiu2ldsWH1Ett7I5/TpJuiSxxdLkS0EMA/APg4gGkAjzLz3xJRF4AhAIsBHAdwJzOfCU7UeFK/HFVlDAhCPTktizlapi21B3uPTsw8tnNlNFOKb+eSMVYDVm4Zgy23LAUg3RTd4MaHfh7A/cz8CyL6PQAHieinAP4UwM+YeZCINgLYCOAbwYmaDKz8hlqWUJlmSAW/YJDPaTOKrB3Dvs1K3Cn242e7aKdMsHxOa0ijFNQ4ulyY+RQz/0J//FsArwIoAPgigMf03R4D0B+UkEnCagm67fbl+PCc5vthCMnjbKmM9UNj2LbnGAY+tdA2DdBMh5bBjoEeHB9c48k3zcCMO8VN7MevgKTdcXJaduZHTXCHJx86ES0G0AvgAICPMfMpoKr0AXzUb+GSitWEFgl+CmbMGU+7Dxax+eal2DHQ46ho//utn5yxZK0Us1GJaUVxsoT1Q2PY+twR3HZNwfYHwa+ApOo4WSJPTe+EKq4VOhF9CMBuAOuZ+Tce3ncPEY0Q0cjExITzG1KAVRaBIKgwuzi+eesyW2vdnJutWg0OXLvQNgh5ZqqM3QerlrrVj4ifAUlVYdIjdy63bBlt7qMuNEJsE4yY2YlIA/A8gD3M/C192zEAn2HmU0Q0H8BLzGz7V+7r6+ORkREfxI43EhgVvEIA3hxcM/N88cYXXO1XT307CjsK+Rz2bbwh8KHoTse3kplQ7cv+cP8y3+SIMkR0kJn7nPZzk+VCAL4H4FVDmes8C+BuAIP6/z9qUtbUIQURglcW5HM1is+qD4qxnx1e2lEY12kr7aLd/Bg4Hd9KZgawc/84+hZ1iVvGhBuXyyoAfwLgBiIa0/99AVVF/jkieg3A5/TnggukIEKwo94ZktOyuP6qeTVuOitl7sYV4sWYaPU69atASSUzA1L+X4ebLJd/ZmZi5k8yc4/+78fM/C4zf5aZr9T/P90OgZPAhtVLItGYSYgeOS2LtSu7Gwpx9h6dsLWsOzs0V0FEt0pa1VfIwI1P2274sxfsZJbVbi1SKRoSknIePQjhd0AslSt44sBbDS4KJ8X1fnna1fHtUhLJ9H99X6H6ZnFuLG+VzF6VsJ0BJKvdWkShtxkZVBFdGNH4oa0wNyhKJ8Xl1vI1Ml+sslyMHuX156BUruD+XYdmFLZby1sls1cl3N9bwNqV3ZauKCn/r0UUuk+4WYIOjxZx/65DkWrKJEQbQ1G6cdO5tXz7ewvKXuSqEvwK88yPi1vL266Pulce7l+G7QM9TQ9nTwvSPtcH6tOqrPpdDI8WseFJ61aigmBHcbKE/t4CRk6cxs7948pVhGH5usksUZX3q7JngAs/Lqr31lvexmf6lfLYSrZNWhAL3QfcLEG3PHuk5WG6QjohVJX0w/3LsHZlt+U+WrY6XNytf1tlPd+1YqHjYAovlnd/bwH7Nt6ANwfXKCciCf4ReQs96KIGP1AtQY0sAbtBu4LghJGe199bqOmKaGbu7Opw8VWDLypb3xquG+P+uWhWZmbfzg4Nm29eiv7eAvoWddkOpvDb8hb8I9IK3Y0rIwrYdaeTilDBDwyjQXU9Gb2A3LS+HTlxGrsPFmsUvzlLxri36qszzVa4uD+iSaRdLn7lsQaN5JWngzD/xgygZ+tPbPdxmw3zxIG3HO8rp8EU5s+UHivRIdIWul95rEHT31vA+qGxsMUQAuK43hvlMkX/lHbh1JFz63NHsPnmpY69WlRBz/r7yskKj8sKOk1E2kL3K4+1WbxYH83ORRSijbmzYdSLWM5MlV3N6VR1WvT6/eKygk4TkbbQrab7tKuYQGV9jJw4jb1HJ3BysoR8h4b3yxWUXFbpCfFj880XBiy4mVLfDBmCr9OqDMvaqkthTsvitmsKDT70Zu4rv1bQcUh8iAuRVuhBRdNVF5B5e8YiH7dUrtTkAbdj1qMQDkZ7VvO1Zjze8uwR3waSrFvZjcf3j/tyrHyutk+63f3Tt6ir5fvKbT66HVaG0/qhMWx59gi23LJUFLtHXPVD94so9EP3YrUI6aRDy9RM/rHC+PEvTpZqep8AaHiuguA8v9MLOwZ6fFGAbi1m1b3kpYLTbjaA12MlGbf90CPtQw8Cld/v8f3joswTQJYIOwZ6GqxVL0y5cKEZYwQL+dxMDxSgGkvZPtDjagYow78A/7q61USzeGl56zYTxg677y/+eO9E2uUSBFHLkBH8w7DoAOC9c+dbOpbTVPt667TCXOOH/t37zp9vBC5VFmo+p+E375dt/et+T+6xC3RanY9W89GdVihyv3ojdRZ61DMVBO/UW4fb9hxDudKaK9FQJKpMJ5Xi2/rYiNTOAAAQ5UlEQVTckernO0Q5DeWvKqPfMdCDsc034lt3Ns70NHJU8jkN+Q4NO/eP+5YD3u5UYbt2voDcr15JnYUeVKaCEA7rLKxTP5SPMfJNlWet+owzU2XbYLnhN6/3S6t81qrAJoBAcsD9CHR6wZB163NHGs6btMf1TuqCokBtQEtFZ4emvDHzOQ1EkuUSNmZl7pShBDi7OAy0DGHbHcuV14hdR0I7jKHLfqAKJrb6GX4EOlv5bElftMa3IdFJxPD7PTh8uKEdqfniVU1WnyyVoWWk2D9s9h6dwIPDh/HCK6dqflyd5m3ardDyOW0mXe5eRfVvM8rcb2szKNeIeUVQ1IdRm4OTQSpY6Q/TOqlU6EDVGth9sNiQbnbbNReKMuzSz6QVbvgUJ0u2OdxZIkwze3JxmPErpbAQgLUZpGvEqjmXlPXHg9QFRQ2sgloM4IVXTs28Lio73kwzN/Th9rKs37B6CbRsaysxAgLpA+7nNCArpKw/nqRWodsFtezGbAnxod5a9ZJjDVQt0bmzW1vEBhlMbDUH3I64NMYTakmFy8XKKrNbTm/bcwx5m6CoEH0Ma9VNOwcj1dDKaj/rssTfKogedJZGkD7ndme7CP6QeAtdZZVdf9U85XuKkyVXhSFCe+js0LBuZbfr6k8CZgqMzH97VTDzzFRZabW7UWCFfA6jD92IHW0cYhx0H/KgXTpCMCQ+bdEuveu9D85bNllqNi1N8A9z+p1VKh0AzJ2dRelcBfWF+kY6o12fELefr/psgzD6jVjJ5HfFqPE5kkYYDRKTttjqRWXnC9w+0GOZcytFR+FCQI0laBWgA4B8x2x86ep5Damnuw8W0beoqyV/r/He+sKefIcG5qorJiwlpwro79w/jr5FXUp5vN5LkkYYPyKt0P2YiGLnC1RV4VlVrQnto75trd2P8t6jEw3ZSEY2hps4CBFgtRgzu1qCUGytGCqq82EeJm31eZKGmHwi7UP3I3VK1Sti6tx5DI8WZ7rmLcjncHKyhK3PHcFZUeahUu82sJtcpVJubuMgzGi7r9hrtk09dn591fmQNMR0EGmF7kfqlJHeVR9QOzNVxqanD+PB4cM1N9eZqXKDT1ZoH3NnN/742gXoVMotS+Sq+Cuf0yzT/wAEFnRsVbnaDSVXnQ9JQ0wHkXa5+JU6ZXTgqw+AGhPQJQAaDbIZwl9+qTGop/Jj3zs0hotzGrQs1XRX9BIHIWp0qbgZP9iK/7xV5drfW8DIidOWbStUKwtJQ0wHkbbQ/UydUt0sosyjwyN3LFcqyP7eAvZtvAHbB3rwfnkak6UyGNW+OuUKY+7sbI2F7XZo96ReSGa2xrc+d8TSgt65f7xpN4kZP4afP9y/DNs9pEnGIQ0x6FTMNOBooRPR9wHcBOAdZv4DfVsXgCEAiwEcB3AnM5/xWzg/Z4qqLBRJUYwGBVOQ2g5VxsvUuQq2141gc9MmuWN2tsEaV6EKvnq9Hv0afu4lWBvUfF6/kKCtPzjmoRPRHwL4HYB/MCn0vwZwmpkHiWgjgE5m/obTh4XZPnd4tIgNTx2qWZprWcLApxbKLNGQMdrVurlxL9v4grLHTn3r2AeHD8+41AhV94rfPdUIwJuDazy/L0453ubzmCXCXSsWAkDNtpWXd+L4uyXf5qOaMXfATCu+5aEz8z8R0eK6zV8E8Bn98WMAXgLgqNBDp/5m1p/P0TKi0EPkQ3NmtZyGCtS61Yxumsbqi2GdnugWVefNZn3QfqdC1v9AXH/VPDx/6JRl4ZwVWQLcDHmqMDd0uKwwY98bp5sR2xWTpTLWD41h/dDYTOdKoHYohij9Kq4qRXWF/rzJQp9k5rzp9TPM3Ol0nDAtdFXVoNsJ7UJwWFm5Kgt2eLSIe4fGLP9mZgu9lSpRoKog5l40ayYI+365glLd8GirKtFWLG/z4BXDFVjQlbMRjDUCwpOl8sw+qlz6pKJlCNMAKorlVhDtisPGrYUeuEInonsA3AMA3d3d15w4ccLVF/Ab1bAKoX3Y/Xh2dmj4oFzBVNk6adQobe9b1IVNT7/iqFztXDNOcpmPpSr9N95jTK+anCrj4pyG986dr3HrmUvyrVwXfYu6ZpS4GBf+YR4YbvcDGxfXV9AK/RiAzzDzKSKaD+AlZnaM6IRpoV+x6ccS/AwJ880VxDxXImDtitpxdPftGvPdXy7Ei3xOwwfnp5Xj9MIct+eVoHu5PAvgbgCD+v8/avI4bUOUeXjM0arZsfXjzfyCGXh8/7jt9CIhfVjFD0rlCu7fdQj3Do0p2yk3k7kUFdykLT6BagD0EiJ6G8BmVBX5LiL6KoBxAHcEKaQfFHwaJyZ458zUhaCWIISNocRVRl6cq2fdZLncpXjpsz7LEigbVi9RBtMEQUgWOS2LOVqmqSZ7ca6ejXSlqJ/09xZEmQtCCsgS4bZrCth881LLxnx2RK161iupUegPDh8OWwRBENpAhRm7D1bbBpgbr2XJuqVZlqgtU6baQaSbc7WKOa9XEIT0YAQ39228YUZBxymrpVkSa6Gbe04LgpA+ipOlmgZfRivtds19DYPEWuiqJk6CIKSH+gZfSR+rl1gLPc6pR4Ig+EPapjIlVqHPSuw3EwTBC2ky7hKp9h4cPgxFSxBBEBKIKoMFiHdeuVcSp9CHR4tSAi4IKSKnZfHIncuxY6An8lOZgibyQVEv3dCGR4u4f9ehNksoCEJYEIDbrqkNdMahe2JQuOq26Bdeuy16yRtVtTkVBCHZdHZoGH3oxrDFCBS33RYj7XKxSj1URa0lTVEQ0skZfdC3EHGFropOW22XAiJBSC9bnj0StgiRINI+dNX8SCNqLaX9giAA1r3P00ikLfQNq5coo9ZS2i8IglBLpC30/t5Cw1CEUrkigxIEIYVkLSYMCbVE2kKXwc6CIBhM2yjzfE5royTRJdIKXRAEAajmm9tVfG65ZWn7hIkwotAFQYg8DOuYGgFYt7I7VcVDdkTahy4IggBUe5cbSjvNlaBOiEIXBCES5HMattyy1LI63OjHkvR+5q0SaYXe2aE1NbVbEIT4QQSxwlsk0gpdMpQEIT1M6sabWOHNE+mg6Fmp/hKE1JCmvuVBEWmFfrHklgpCoshpWaxb2Z36vuVBEWmXS7kiY4cEoRnmzs6idK6CKNxB+ZyGs6VyjT+8b1GX+MkDINIK/b1z0g5XEJrhvXMVZAjVBO6Qee/ceWwf6KlR2OInD4ZIu1wEQWie6QgocwAoVxgPPHM4bDFSQaQVek6LtHiCILjkvXMVGULRBiKtMefUBU4EQYgvVpPGBH+JtEKXoiJBSA6qCWSCf0RaoQuCkBwkzzx4WspyIaLPA/hbAFkA32XmQV+kEgQhcRQnSzUzDjo7NGy+udr21hglaQyxKJhSGY1Rk5Li6EzTCp2IsgC+DeBzAN4G8DIRPcvM/+KXcIIgJJczU2Xc/+QhZACU9ZQcYyJRcbKETU8fxsiJ09h9sDjTrMvYDkCUugWtuFyuBfA6M/+Kmc8B+CGAL/ojliAIaaAyzTPKvJ5SuYInDrxV03nR2C4BVmtaUegFAG+Znr+tb6uBiO4hohEiGpmYmGjh4wRBSBuqGaISYLWmFYVOFtsazj4zP8rMfczcN2/evBY+ThCEtJElKzUjAVYVrSj0twEsND2/FMDJ1sQRBCFNZDMELWOttHNaFnetWCiNvDzQSpbLywCuJKLLABQBfBnAV3yRShAE12QJmD0rg1K52oorIi1cHHGb5SKNvNzTtEJn5vNE9F8B7EE1bfH7zHzEN8lckiXCXSsW4uH+Ze3+aEEQfMJOQUsjL/e0lIfOzD8G8GOfZGng+OCamrxVgx11ndsEQRCEiLfPBapKXRAEQXBGSv8FQRASgih0QRCEhCAKXRAEISGIQhcEQUgIotAFQRASArGiV0IgH0Y0AeBEk2+/BMCvfRQnSETWYBBZgyEussZFTsB/WRcxs2PvlLYq9FYgohFm7gtbDjeIrMEgsgZDXGSNi5xAeLKKy0UQBCEhiEIXBEFICHFS6I+GLYAHRNZgEFmDIS6yxkVOICRZY+NDFwRBEOyJk4UuCIIg2BALhU5EnyeiY0T0OhFtDFmWhUS0l4heJaIjRPQ1fXsXEf2UiF7T/+/UtxMR/Q9d9leI6OoQZM4S0SgRPa8/v4yIDuiyDhHRbH37Rfrz1/XXF7dZzjwRPUVER/Xze11UzysR3av//X9JRE8Q0ZyonFci+j4RvUNEvzRt83weiehuff/XiOjuNsq6Tb8GXiGiZ4gob3ptky7rMSJabdoeuI6wktX02n8jIiaiS/Tn4ZxXZo70P1R7rb8B4HIAswEcAvCJEOWZD+Bq/fHvAfh/AD4B4K8BbNS3bwTwV/rjLwD4R1TnDqwEcCAEme8D8AMAz+vPdwH4sv74OwD+XH/8XwB8R3/8ZQBDbZbzMQD/WX88G0A+iucV1dm5bwLImc7nn0blvAL4QwBXA/ilaZun8wigC8Cv9P879cedbZL1RgCz9Md/ZZL1E/r9fxGAy3S9kG2XjrCSVd++ENW5ECcAXBLmeW3LDdDiSbwOwB7T800ANoUtl0meHwH4HIBjAObr2+YDOKY//nsAd5n2n9mvTfJdCuBnAG4A8Lx+gf3adMPMnF/9orxOfzxL34/aJOeHdSVJddsjd15xYUB6l36engewOkrnFcDiOiXp6TwCuAvA35u21+wXpKx1r30JwE79cc29b5zXduoIK1kBPAVgOYDjuKDQQzmvcXC5GDePwdv6ttDRl869AA4A+BgznwIA/f+P6ruFLf8OAF8HMK0//wiASWY+byHPjKz662f1/dvB5QAmAPwv3T30XSKaiwieV2YuAvgbAOMATqF6ng4imufVwOt5DPu6NfhPqFq6QARlJaJbABSZ+VDdS6HIGgeFbjVBNvTUHCL6EIDdANYz82/sdrXY1hb5iegmAO8w80GX8oR5rmehupz9O2buBfAeqq4BFWGe104AX0R12b8AwFwAf2wjTySvYR2VbKHLTEQPADgPYKexyWK30GQlog4ADwB4yOpli22ByxoHhf42qj4qg0sBnAxJFgAAEWmoKvOdzPy0vvnfiGi+/vp8AO/o28OUfxWAW4joOIAfoup22QEgT0TGtCqzPDOy6q9fDOB0m2R9G8DbzHxAf/4Uqgo+iuf1jwC8ycwTzFwG8DSATyOa59XA63kM9b7Tg4U3AVjLum/CRqawZL0C1R/1Q/o9dimAXxDRx8OSNQ4K/WUAV+oZBLNRDSo9G5YwREQAvgfgVWb+lumlZwEYEeu7UfWtG9v/gx71XgngrLH0DRpm3sTMlzLzYlTP24vMvBbAXgC3K2Q1vsPt+v5tscqY+V8BvEVES/RNnwXwL4jgeUXV1bKSiDr068GQNXLn1YTX87gHwI1E1KmvSG7UtwUOEX0ewDcA3MLMU3Xf4ct61tBlAK4E8HOEpCOY+TAzf5SZF+v32NuoJkz8K8I6r0EEDgIIRHwB1WySNwA8ELIs/x7VJdIrAMb0f19A1Sf6MwCv6f936fsTgG/rsh8G0BeS3J/BhSyXy1G9EV4H8CSAi/Ttc/Tnr+uvX95mGXsAjOjndhjVLIBInlcAWwEcBfBLAP8b1cyLSJxXAE+g6tsvo6pkvtrMeUTVf/26/u8/tlHW11H1Mxv313dM+z+gy3oMwB+btgeuI6xkrXv9OC4ERUM5r1IpKgiCkBDi4HIRBEEQXCAKXRAEISGIQhcEQUgIotAFQRASgih0QRCEhCAKXRAEISGIQhcEQUgIotAFQRASwv8Hlrl3JK4NTQoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter( data_train['实发辐照度'], data_train['实际功率'] ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183093 entries, 0 to 42686\n",
      "Data columns (total 9 columns):\n",
      "时间       183093 non-null object\n",
      "辐照度      183093 non-null float64\n",
      "风速       183093 non-null float64\n",
      "风向       183093 non-null int64\n",
      "温度       183093 non-null float64\n",
      "压强       183093 non-null float64\n",
      "湿度       183093 non-null float64\n",
      "实发辐照度    183093 non-null float64\n",
      "实际功率     183093 non-null float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 14.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46571 entries, 0 to 13893\n",
      "Data columns (total 8 columns):\n",
      "id     46571 non-null int64\n",
      "时间     46571 non-null object\n",
      "辐照度    46571 non-null float64\n",
      "风速     46571 non-null float64\n",
      "风向     46571 non-null int64\n",
      "温度     46571 non-null float64\n",
      "压强     46571 non-null float64\n",
      "湿度     46571 non-null float64\n",
      "dtypes: float64(5), int64(2), object(1)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>辐照度</th>\n",
       "      <th>风速</th>\n",
       "      <th>风向</th>\n",
       "      <th>温度</th>\n",
       "      <th>压强</th>\n",
       "      <th>湿度</th>\n",
       "      <th>实发辐照度</th>\n",
       "      <th>实际功率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "      <td>183093.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.589675</td>\n",
       "      <td>-0.638823</td>\n",
       "      <td>169.799544</td>\n",
       "      <td>-0.015864</td>\n",
       "      <td>0.098247</td>\n",
       "      <td>-0.087954</td>\n",
       "      <td>217.638943</td>\n",
       "      <td>4.846102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.549945</td>\n",
       "      <td>0.286773</td>\n",
       "      <td>96.834579</td>\n",
       "      <td>0.395048</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.455642</td>\n",
       "      <td>320.772498</td>\n",
       "      <td>9.499288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>-0.397667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.849057</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>-0.337374</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>-0.458333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.965779</td>\n",
       "      <td>-0.716981</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>-0.042424</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>-0.145833</td>\n",
       "      <td>0.711000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.205323</td>\n",
       "      <td>-0.509434</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>382.333000</td>\n",
       "      <td>5.702330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1419.200000</td>\n",
       "      <td>48.834700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 辐照度             风速             风向             温度  \\\n",
       "count  183093.000000  183093.000000  183093.000000  183093.000000   \n",
       "mean       -0.589675      -0.638823     169.799544      -0.015864   \n",
       "std         0.549945       0.286773      96.834579       0.395048   \n",
       "min        -1.000000      -1.000000       0.000000      -1.000000   \n",
       "25%        -1.000000      -0.849057      96.000000      -0.337374   \n",
       "50%        -0.965779      -0.716981     125.000000      -0.042424   \n",
       "75%        -0.205323      -0.509434     275.000000       0.292929   \n",
       "max         1.000000       1.000000     359.000000       1.000000   \n",
       "\n",
       "                  压强             湿度          实发辐照度           实际功率  \n",
       "count  183093.000000  183093.000000  183093.000000  183093.000000  \n",
       "mean        0.098247      -0.087954     217.638943       4.846102  \n",
       "std         0.301025       0.455642     320.772498       9.499288  \n",
       "min        -1.000000      -1.000000      -0.020000      -0.397667  \n",
       "25%        -0.090909      -0.458333       0.000000      -0.018000  \n",
       "50%         0.090909      -0.145833       0.711000       0.000000  \n",
       "75%         0.333333       0.263158     382.333000       5.702330  \n",
       "max         1.000000       1.000000    1419.200000      48.834700  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>辐照度</th>\n",
       "      <th>风速</th>\n",
       "      <th>风向</th>\n",
       "      <th>温度</th>\n",
       "      <th>压强</th>\n",
       "      <th>湿度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46571.00000</td>\n",
       "      <td>46571.000000</td>\n",
       "      <td>46571.000000</td>\n",
       "      <td>46571.000000</td>\n",
       "      <td>46571.000000</td>\n",
       "      <td>46571.000000</td>\n",
       "      <td>46571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23286.00000</td>\n",
       "      <td>-0.529024</td>\n",
       "      <td>-0.639687</td>\n",
       "      <td>132.248051</td>\n",
       "      <td>0.355123</td>\n",
       "      <td>0.026480</td>\n",
       "      <td>0.319028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13444.03403</td>\n",
       "      <td>0.580424</td>\n",
       "      <td>0.227123</td>\n",
       "      <td>80.058099</td>\n",
       "      <td>0.207354</td>\n",
       "      <td>0.261409</td>\n",
       "      <td>0.389574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.256566</td>\n",
       "      <td>-0.696970</td>\n",
       "      <td>-0.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11643.50000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.801887</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>-0.151515</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23286.00000</td>\n",
       "      <td>-0.857434</td>\n",
       "      <td>-0.679245</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34928.50000</td>\n",
       "      <td>-0.098963</td>\n",
       "      <td>-0.518868</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>0.503030</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46571.00000</td>\n",
       "      <td>0.961977</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>0.904167</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                id           辐照度            风速            风向            温度  \\\n",
       "count  46571.00000  46571.000000  46571.000000  46571.000000  46571.000000   \n",
       "mean   23286.00000     -0.529024     -0.639687    132.248051      0.355123   \n",
       "std    13444.03403      0.580424      0.227123     80.058099      0.207354   \n",
       "min        1.00000     -1.000000     -1.000000      0.000000     -0.256566   \n",
       "25%    11643.50000     -1.000000     -0.801887     90.000000      0.232323   \n",
       "50%    23286.00000     -0.857434     -0.679245    104.000000      0.354167   \n",
       "75%    34928.50000     -0.098963     -0.518868    129.000000      0.503030   \n",
       "max    46571.00000      0.961977      0.636364    359.000000      0.904167   \n",
       "\n",
       "                 压强            湿度  \n",
       "count  46571.000000  46571.000000  \n",
       "mean       0.026480      0.319028  \n",
       "std        0.261409      0.389574  \n",
       "min       -0.696970     -0.812500  \n",
       "25%       -0.151515      0.043478  \n",
       "50%        0.000000      0.375000  \n",
       "75%        0.212121      0.625000  \n",
       "max        0.857143      1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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